An analysis of the significance of the Tre2/Bub2/CDC 16 (TBC) domain protein family 8 in colorectal cancer

The TBC (Tre-2/Bub2/Cdc16, TBC) structural domain is now considered as one of the factors potentially regulating tumor progression. However, to date, studies on the relationship between TBC structural domains and tumors are limited. In this study, we identified the role of TBC1 domain family member 8 (TBC1D8) as an oncogene in colorectal cancer (CRC) by least absolute shrinkage and selection operator (LASSO) and Cox regression analysis, showing that TBC1D8 may independently predict CRC outcome. Functional enrichment and single-cell analysis showed that TBC1D8 levels were associated with hypoxia. TBC1D8 levels were also positively correlated with M2 macrophage infiltration, which may have a complex association with hypoxia. Taken together, these results show that the TBC1D8 gene is involved in colorectal carcinogenesis, and the underlying molecular mechanisms may include hypoxia and immune cell infiltration.

Immunohistochemical (IHC) staining. IHC was conducted according to a previously published protocol 22 . Sections were blocked with blocking solution and incubated with anti-TBC1D8 antibodies. The extent and intensity of staining were assessed by two independent investigators. Staining intensities were graded as 0 (none), 1 (weak), 2 (moderate), and 3 (strong). Staining extent was graded as 0 (no positively-stained cells), 1 (less than 10%), 2 (10-50%), and 3 (over 50%). The histochemistry score (H-SCORE), representing both the proportion of stained cells and the degree of staining, was determined as: "H-SCORE = ∑ (PI × I) = (percentage of cells with weak intensity × 1) + (percentage of cells with moderate intensity × 2) + (percentage of cells with strong intensity × 3)", where PI represents the percentage of positive cells to the total number of cells in a particular field and I represent the intensity of staining. The H-SCORE ranged between 0 and 300, with higher scores indicating more intense staining. Establishment of hypoxia model. A stock solution of CoCl 2 was prepared by dissolving solid CoCl 2 in serum-free DMEM. Using a previously published method 23 , we pretreated cells with 300 µmol/L CoCl 2 for 30 h. CoCl 2 induces hypoxia through blocking degradation of HIF-α, activating the hypoxia cascade 24 .
TBC1D8 expression assessment. The Western blotting method followed previous descriptions 25 . Proteins were lysed in RIPA buffer and protein concentrations were measured by the Bradford assay. Samples of 20 µg each were separated on 10% or 8% SDS-PAGE, transferred to polyvinylidene fluoride (PVDF) membranes, and blocked with 5% bovine serum albumin. The blots were then probed with the relevant primary antibodies at 4 °C overnight. Blots were washed in Tris-buffered saline containing 0.05% Tween-20 and were incubated with the corresponding secondary antibodies with an Electrochemiluminescence (ECL) detection kit used to measure densities. The protein bands (including β-actin as the loading control) were visualized with a gel image processing system (ChemiDoc XRS +) and relative concentrations were determined. The expression levels of each protein were normalized relative to β-actin protein expression and are shown as relative protein levels. Relative protein expression = gray value of target protein band/gray value of β-actin band. TBC1D8 expression in CRC was first investigated using the Tumor Immune Estimation Resource (TIMER) web tool (https:// cistr ome. shiny apps. io/ timer/) Cancer Cell Line Encyclopedia (CCLE) database (https:// porta ls. broad insti tute. org/ ccle); The Human Protein Atlas (HPA) (https:// www. prote inatl as. org/) and the Gene Expression Profiling Interactive Analysis (GEPIA) database (https:// www. oncom ine. org/). HCT116 and HT-29 cells which express relatively high levels of TBC1D8 were selected for subsequent experiments. The plasmids described below were all constructed by GeneChem (Shanghai, China). The procedures for constructing RNAi plasmids are described in the Supplementary material. From three short hairpin interfering RNA (shRNA) targeting TBC1D8, we selected the one with the highest inhibitory efficiency for the subsequent experiments. The shRNA-TBC1D8 plasmid (sh-TBC1D8) and the control (NC) non-targeting sequence plasmid were transfected into 70%-confluent CRC cells using Lipofectamine 3000 per provided protocols. The transduction efficiencies were examined by western blotting and Green fluorescent protein (GFP) expression. www.nature.com/scientificreports/ Colony formation assays. The ability of cells to form colonies was measured as previously described 26 .

RNAi plasmids construction and transfection.
Five hundred cells per well were plated in 6-well plates and grown for approximately 14 days. Colonies (a minimum of five cells) were stained with 0.5% crystal violet at room temperature (20-25 °C)  Sphere formation. Sphere formation was examined as previously described 27 . Single-cell suspensions were plated in 6-well ultralow attachment plates (5 × 10 3 cells/well) and grown in serum-free medium with Human recombinant Fibroblast growth factor (FGF) (20 ng/ml), Human recombinant Epidermal Growth Factor (EGF) (20 ng/ml), and 2% B27. The numbers and sizes of the spheres were determined after seven days under light microscopy.
Co-culture system. Cell slides were placed at the bottom of the 6-well plates at the beginning of the coculture experiment. THP-1 monocytic cells (1 × 10 5 cells/mL) were incubated with 10 ng/mL PMA for 48 h to allow differentiation into M0 macrophages 27 The medium was then replaced with serum-free DMEM and the cells were allowed to grow for 24 h. Co-cultures between M0 and CRC were conducted using Transwell inserts (0.4 µm) in which culture medium was diffusible but cells were not permeable. To mimic the interactions between macrophages and tumors, 6 × 10 5 CRC cells were placed in the upper chamber of a 6-well transwell apparatus and the M0 macrophages were placed in the lower chamber.
Immunofluorescence. Immunofluorescence staining was conducted according to a previously published protocol 28 . Briefly, the cells cultured on cover slips were fixed, permeabilized with 0.5% Triton X-100, and incubated with anti-CD163 and anti-CD206 polyclonal antibodies overnight at 4 °C. Subsequently, the slides were incubated with secondary antibodies. Nuclei were stained using 4' ,6-diamidino-2-phenylindole (DAPI) for 3 min, and the cells were incubated in the dark for 3 min. The slides were washed with PBS four times, for 5 min each. Then, the slides were sealed with sealing solution containing a fluorescence quencher and observed and imaged under a fluorescence microscope. Immunofluorescence was examined under an epi-fluorescence microscope (Olympus CKX-41,). By changing the filters, the different secondary antibodies could be identified in double-stained sections. Images were captured with a digital camera (Olympus, DP50).

Transcriptomic expression and survival analysis. TBC1D8 expression was investigated using the The
Cancer Genome Atlas (TCGA)-Colon Adenocarcinoma (COAD) cohort and the GSE10950 and GSE37182 datasets 29 . Expression levels were analyzed in terms of different parameters, namely, "T (Tumor)", "N (Node)", and "M (Metastasis)" stages using the TCGA-COAD data. Survival analysis, specifically, the Overall Survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were performed by log-rank test using the TCGA-COAD data.
LASSO and cox regression. Raw RNA-seq and clinical data were acquired from the TCGA dataset in January 2020. LASSO regression analysis was conducted using the R package 'glmnet' to determine the research objective. Univariate and multivariate Cox regression was performed to determine the independent prognostic factors of TBC1D8. The forest plot representing the P-value, hazard ratios (HRs), and 95% confidence intervals (CIs) of each variable were determined using the "forestplot" package in R.
Function analysis of TBC1D8-associated genes. The median cutoff of TBC1D8 expression in the TCGA-COAD was used to differentiate high and low TBC1D8 groups. The R "limma" package was used to identify TBC1D8-associated genes 30 . Enrichment analysis was conducted with the "clusterProfiler" package in R to examine possible TBC1D8 functions 31 .
For Gene Set Variation Analysis (GSVA) 33 , the R package "GSVA" was used to conduct enrichment analysis and "Limma" was used to identify differentially enriched genesets based on TCGA-COAD and GSE37182. To further explore the association between TBC1D8 and hypoxia, we defined 29 prognosis-related hypoxia genes with reference to the work of Joon-Hyop Lee et al. 34 . and calculated the correlation between them and TBC1D8. The Tumor Immune Single Cell Hub (TISCH) 35 based GSE146771 data and the CancerSEA 36 were used for the single-cell level comprehensive exploration for cancer cell function 37  www.nature.com/scientificreports/ Immune analysis. The association between TBC1D8 level and immune infiltration was examined with the Estimating the Proportion of Immune and Cancer cells (EPIC) algorithm based on TCGA-COAD 38 . TIMER (TCGA-COAD) was used for reliable assessment of infiltration, pairwise comparisons were quantified by Spearman's rank correlation, with P < 0.05 representing significance. CIBERSORT was used to confirm the relationship between TBC1D8 levels and macrophage polarization 39 based on. GSE10950 and GSE37182 All results were processed using the "ggplot2" and "pheatmap" packages in R.
DNA methylation and genetic alternation analysis. The analysis of TBC1D8 DNA methylation and the correlation between disease prognosis and TBC1D8 methylation values was performed using the Methsurv and MEXPRESS databases (http:// mexpr ess. be). We also used the SurvivalMeth database 40  We provided data of genomic alteration type, mutation site profile analyses. In addition, we analyzed the differential expression of macrophage markers in the wild-type and mutant TBC1D8 groups using the TIMER database (https:// cistr ome. shiny apps. io/ timer/).

Statistical analysis.
Data were expressed as means ± SEM. Comparisons between two groups and multiple groups were assessed by t-tests and one-way ANOVA, respectively. All data were analyzed with SPSS 29.0 (IBM Corp., Armonk, NY, USA) and visualized with GraphPad Prism 8.0 (GraphPad Software, Inc., USA). Experiments were conducted a minimum of three times. ****P < 0.0001, ***P < 0.001, **P < 0.01 and *P < 0.05 were defined to be statistically significant.

Prognostic value of TBC domain family members.
A total of 44 TBC structural domain family members were used for LASSO regression to identify robust markers for follow-up studies. Cross-validation was performed in ten rounds to prevent overfitting (Fig. 1A,B). Sequential Cox regression (both univariate and multivariate) indicated that TBC1D8 was significantly correlated with CRC prognosis, identifying it as the research target ( Fig. 1C-F). Finally, Kaplan-Meier survival curves showed that patients with higher TBC1D8 levels had shorter OS and PFI (P < 0.05, Fig. 1G-H) and we did not observe an association between TBC1D8 and DSS (P > 0.05, Figure S1). These results suggested that TBC1D8 is closely associated with the progression of CRC and had potential research value.
Analysis of TBC1D8 expression. Data from TCGA-COAD indicated higher levels of TBC1D8 transcription in CRC tissue ( Fig. 2A). Moreover, information from the GSE10950 and GSE37182 datasets also showed higher TBC1D8 levels in CRC compared to paracancerous tissues ( Fig. 2B,C). It was found that there was no significant link between TBC1D8 levels and TNM staging (   2N). Results strongly indicated that an imbalance in the TCB1D8 gene expression can cause CRC.

Prediction of TBC1D8 functions and interactions. The network linking neighboring genes with
TBC1D8 was compiled by GeneMANIA. This shows various relationships, including interactions, colocalization, and common pathways (Fig. 3A). The genes co-expressed with TBC1D8 in TCGA-COAD were examined using R "limma" (Fig. 3B). In all, 521 differentially expressed genes (DEGs) were used for the protein-protein interaction network (PPI) assembly. In the network, SOX2 has the highest score as a hub gene based on cytoHubba (Fig. 3C). Enrichment analysis based on SOX2 as the core sub-network showed that the network may involve "regulation of peptidase activity", "cell-cell adhesion via plasma-membrane adhesion molecules", "humoral immune response", "maintenance of gastrointestinal epithelium", "digestive system process", and "regulation of transmembrane receptor protein serine/threonine kinase signaling pathway" (Fig. 3D,E). GSEA for the whole PPI showed that TBC1D8 may be associated with hypoxia, angiogenesis, and matrix degradation (Fig. 3F,G). These findings offered evidence regarding the relationship between TBC1D8 and cancer-related signaling pathway. This led us to perform a more in-depth molecular mechanism study. www.nature.com/scientificreports/ Gene Set Variation Analysis (GSVA) based on TBC1D8 expression. The above results suggested that TBC1D8 may be closely associated with the hypoxia phenotype, and here we performed a hallmark genesetbased GSVA to further analyze the possible involvement of TBC1D8 in cancer-related pathways. The distribution of GSVA scores for the CRC samples in TCGA and GSE37182 is shown in Fig. 4A,B. The results of the differential score analysis based on the median TBC1D8 levels showed that "HYPOXIA" was active in the high-TBC1D8 group, in both cohorts (P < 0.05) (Fig. 4C,D). The relationships between TBC1D8 and hypoxia- TBC1D8 can be induced by hypoxic conditions, thereby contributing to tumor proliferation and tumor stemness. The enrichment, GSVA, and single-cell analysis results suggested that TBC1D8 may be associated with hypoxia. Given that its Hub Gene is an indicator of SOX2, which is involved in the development and maintenance of stem-like properties in cancer cells, we considered that TBC1D8 may also be associated with tumor cell stemness. A possible mechanism is proposed in Fig. 6A. Based on the above results we performed a preliminary experimental validation. To simulate neoplastic hypoxic microenvironmental conditions, we used CoCl 2 to induce a hypoxic environment. It was found that TBC1D8 expression gradually increased with prolongation of the CoCl 2 treatment (Fig. 6B,C). Western blot analysis was used to determine levels of hypoxia-inducible factor (HIF)-1α to confirm the induction of hypoxia ( Figure S2). High levels of TBC1D8 were seen in HCT116 and HT-29 cells, so these were used for further study. Transfection efficiencies were confirmed by GFP intensity and western blotting (Fig. 6D,E). Silencing of TBC1D8 reduced clone formation (Fig. 6F). The schematic diagram of subcutaneous tumor models is presented in Fig. 6G. Moreover, TBC1D8 knockdown suppressed xenograft tumor growth in vivo (Fig. 6H,I). IHC staining of xenograft tumors revealed a significant decrease in cytosolic Ki-67 expression accompanied by knockdown of TBC1D8 (Fig. 6J). Tumor sphere formation is documented to be linked with the stemness of cancer cells. Quantification of spheres showed that TBC1D8 knockdown significantly reduced both the numbers (Fig. 6K) and sizes (Fig. 6L) of CRC cell tumor spheres. The expression levels of SOX2 were significantly increased following exposure to hypoxia compared with those under normoxic conditions (Fig. 6M). The expression levels of SOX2 were markedly downregulated following TBC1D8 knockdown (Fig. 6N). This part of the results suggested that TBC1D8 plays an important role in linking hypoxia and stem cell characteristics of CRC cells.
Immune microenvironment analysis of TBC1D8. In this section, we explored the relationship between immune infiltration and CRC pathogenesis. Relationships between TBC1D8 and infiltration were examined with the EPIC algorithm. This showed that macrophages, neutrophils, CD8 T cells, and T helper cells were present at higher levels when TBC1D8 was strongly expressed (Fig. 7A). TIMER was also used to explore potential correlations between TBC1D8 levels and immune cell infiltration (Fig. 7B). There were positive correlations between  (Fig. 7C,D). Finally, we further found that M2 macrophages had a higher degree of infiltration in CRC patients in the TBC1D8 high expression group using GSE10950 and GSE37182 (Fig. 7E,F). This series of results suggested the presence of a positive association between TBC1D8 expression and M2 macrophage infiltration. To further investigate the influence of TBC1D8 expression on M2 macrophage abundance in CRC, we established a tumor-macrophage co-culture model using a transwell non-contact co-culture unit (Fig. 7G). We observed that TBC1D8 knockdown significantly down  www.nature.com/scientificreports/ -regulated the surface markers of M2 tumor-associated macrophages (TAMs) (CD206 and CD163) in THP-1 macrophages (Fig. 7H,I). Our results suggested that TBC1D8 can exert immunosuppressive functions by promoting the proliferation of M2 macrophages.

Genetic alteration analysis and DNA methylation of TBC1D8.
Here, we attempted to investigate the potential mechanism of TBC1D8 in CRC in terms of mutation and DNA methylation. We first detected the mutation frequency in 8 groups of CRC cases through the cBioPortal database (  (Fig. 8D), which all point to macrophages. Subsequently, M1 and M2 macrophage markers were analyzed under TBC1D8 (mutated and wild) status (Fig. 8E,F). Differential methylation status of TBC1D8 between CRC tissues and normal tissues were analyzed. The statistically significant CG sites are shown in the Fig. 8G. Based on methylation data from TCGA-COAD, we established that the methylation values obtained from 9 methylation probes were significantly correlated with TBC1D8 expression levels (Fig. 8H). The intersection of CG sites in the above two results (Fig. 8I) were then used to perform a survival analysis and cg20893936 was identified to be related to CRC patients' survival (Fig. 8J). These results suggested that TBC1D8 gene DNA alternation may affect colorectal tumorigenesis.

Discussion
CRC is a heterogeneous disease, which occurs and progresses in a complex microenvironment 41 . Hypoxia is an important feature of the colorectal cancer microenvironment, and at the same time, hypoxia makes tumor signaling pathways more intricate 42,43 . In this complex molecular network, some genes are upregulated under hypoxic conditions and can contribute to the malignant biological behavior of tumor cells through multiple signaling cascades 44,45 . This also means that there are more opportunities to find effective targets for action. With the support of computational disciplines such as bioinformatics and systems biology, our group has used several publicly available online databases to initially identify an oncogene that has not been previously mentioned in colorectal cancer. This provides a potentially interesting direction for the development of therapeutic, targeted drugs for colorectal cancer. Several studies have stated that a dysregulated TBC family expression is involved in various human diseases such as cancer, obesity and X-linked early-onset nephrotic syndrome and so on [46][47][48][49] . There are few reports on TBC1 domain family members in tumor, but the available studies suggest that it mostly functions as an oncogene in tumors. TBC1D3 overexpression inhibits the ubiquitination and degradation of Epidermal growth factor receptor (EGFR), thereby enhancing the EGFR signaling pathway and promoting cell proliferation 50 . TBC1D15 has been identified as a powerful oncogene in hepatocellular carcinoma, and the mechanisms involved include promotion of Tumor-initiating-stem-like cells self-renewal and p53 deletion 51 . In addition, TBC1D23 has been suggested to be a powerful promoter of tumor cell proliferation, migration, and invasion in non-small cell lung cancer 52 . In conclusion, most of the available studies reveal a possible fact that TBC1 domain family members can participate in malignant cell transformation or metastasis by regulating Rab proteins. However, to date, few studies have been reported on its involvement in colorectal cancer.
In this study, we first analyze the LASSO regression based on TCGA-COAD, and showed that TBC1D8 and TBC1D17 were potentially valuable research targets. While it has been reported that TBC1D8 can regulated metabolic reprogramming to drive ovarian cancer progression, little research has been reported on TBC1D17 in cancer. TBC1D17 is currently identified as a mitochondria-localized RAB7A GTPase-activating protein (GAP) that helps maintain mitochondrial homeostasis by regulating RAB7A activity and limiting mitophagy. And a work by Xi Sheng Rao et al. demonstrated that TBC1D17 can act as a molecular bridge linking AMPK and Rab5 to play the role of an GTPase activating protein to regulate glucose homeostasis. Further Cox analysis incorporating TNM staging suggested that TBC1D8 may be an independent prognostic factor for CRC, and therefore we selected TBC1D8 as our study target. The results of three independent CRC datasets showed significantly   www.nature.com/scientificreports/ elevated transcription of TBC1D8 in CRC tissues and, the HPA database and our samples further validated the overexpression of TBC1D8 in colorectal cancer at the protein level. In order to explore the possible biological function of TBC1D8, we identified 578 significantly related genes based on TCGA-COAD and performed a functional enrichment analysis. The results showed that TBC1D8 and its related genes are significantly associated with many related to the tumor microenvironment, including "ANGIOGENESIS", "DEGRADATION OF THE EXTRACELLULAR MATRIX", "ACTIN BINDING", and "INTEGRIN BINDING". All these processes are important components of the tumor microenvironment, and they can interact to further promote tumor growth and metastasis. It is worth noting that we have observed that TBC1D8 may also be related to the hypoxic phenotype, and by analyzing the TBC1D8-based PPI, we found that SOX2, a hypoxia-related indicator, was the highest-scoring hub gene. It has been well documented that SOX2 can promote tumor proliferation and invasion and can regulate tumor cell stemness in a hypoxic microenvironment through a variety of pathways 53 . Cell sphere formation capacity have been used as read-out for tumor stemness 54 . Previous studies have reported a less differentiated phenotype and/or an increase in stemness induced by hypoxia in Various tumors [55][56][57] . In the present study, it was shown that TBC1D8 knockdown potentiated sphere-forming capacity in CRC cells. However, the correlation between SOX2 and TBC1D8 was poor. This implies that TBC1D8 may be performing a similar biological function to SOX2 although through different mechanisms. On the one hand, hypoxia leads to a transcriptional program promoting basement membrane degradation, while increasing the ab initio synthesis of protofibrillar collagen as a physical pathway of tumor invasion 58,59 ; on the other hand, hypoxia is a potent promoter of tumor-associated angiogenesis 60 , which was significantly activated in the high TBC1D8 expression group in the GSVA. Single-cell level analysis also suggested that cells expressing TBC1D8 were enriched for hypoxia-associated genes. We calculated the correlation between TBC1D8 and hypoxia-related genes, however, no consistent results were observed in two independent datasets, which may be due to heterogeneity between samples. We then used a cellular model of hypoxia to verify the upregulation of TBC1D8 under hypoxic conditions. Based on these results, we subsequently verified the biological functions of TBC1D8 in CRC cells. This showed that blocking TBC1D8 expression inhibited CRC cell proliferation, decreased clonogenic formation, invasion, and stemness in vitro. In addition, growth factor secretion induced by the hypoxic tumor environment also promotes macrophage aggregation, and macrophages are sensitive to hypoxia and alter their gene expression accordingly. Recent studies have found that macrophages in hypoxic regions induce fibrosis by growth factor production, thereby attracting both additional macrophages and mesenchymal cells 61,62 . Based on this observation, we examined the link between TBC1D8 and macrophage infiltration, finding that CRC patients with high TBC1D8 expression tended to have higher levels of M2 macrophage infiltration. Subsequent results also supported this conclusion. Because hypoxia broadly affects molecular events involved in cancer progression, aggressiveness, and treatment resistance, targeting hypoxia is an attractive approach for solid cancer treatment 63 . However, in practice, it is difficult to quantify hypoxia specifically, while targeting specific hypoxia-related targets is a feasible option. On the one hand, hypoxia can help tumor cells maintain stem cell-like characteristics to enhance their aggressiveness 64 , while on the other hand, immunosuppressive cells, including M2 macrophages 65,66 , accumulate in hypoxic regions of the tumor where they promote tumor progression and activate immune tolerance mechanisms that enable cancer cells to evade host immune surveillance 67,68 . Thus, targeting the hypoxia-related molecular signaling cascade network not only helps to improve tumor resistance to conventional chemotherapy, but also increases the probability that tumor cells will be recognized and killed by the body's immune system.
Altered genomic status is strongly associated with CRC carcinogenesis 69 . We observed that that TBC1D8 has a reasonable chance of being mutated in CRC. Functional enrichment suggested that TBC1D8 mutations may be associated with macrophage activation, and further calculations also showed that MSR1 and MS4A4A tend to be more strongly expressed in patients with mutated TBC1D8 in CRC. MSR1 and MS4A4A proteins have been shown to be associated with the activation of both tumor-associated and M2 macrophages 70 . Accordingly, we hypothesize that mutant TBC1D8 may be more able to activate immunosuppressive macrophages. Considering that DNA methylation is the most important method of epigenetic modification, we also observed that the degree of methylation of cg20893936 may correlate with the prognosis of CRC patients and can serve as a diagnostic biomarker. Further investigation into the question of how methylation of different sites in TBC1D8 affects the outcomes of CRC patients is warranted. Figure 8. The genetic alteration of TBC1D8 in CRC. (A) Frequencies of TBC1D8 mutations and copy number alterations (CNA) in the 8 datasets shown on the right side. (B) Volcano map of genes showing differential expression after a change in TBC1D8 (mutated and wild). Red dots, upregulated genes; blue dots, downregulated genes; abscissa, differences in gene expression (log2 fold change); and ordinate, significance of these differences (− log10 padj). (C) The mutation site profile of the TBC1D8 gene is shown. (D) GSEA was used to determine the functions of differential gene sets between the mutated and wild groups based on GO. Only gene sets with Normal P < 0.05 and FDR < 0.1 were considered significant and displayed in the plot. The x axis represents the distribution of log-fold change (logFC) corresponding to the core molecules in each gene set. (E-F) Differential Analysis of TBC1D8 (mutated and wild) with M1 (E) and M2 (F) macrophage markers. Wilcoxon-Mann-Whitney test was performed based on TIMER database. (G) The Differential Analysis of the TBC1D8 probe methylation were indicated. (H) Waterfall plot of the methylation levels in the TBC1D8 gene. The correlations between TBC1D8 methylation or expression levels were also analyzed. (I) Venn diagram showing the intersection of (G,H). (J) Survival analysis based on the intersection methylation probes; P < 0.05 was considered statistically significant. www.nature.com/scientificreports/ In conclusion, in this study, we found that TBC1D8 is a potential marker for both CRC diagnosis and prognosis. TBC1D8 may function as an oncogene triggered by hypoxia, a result that could lay the foundation for subsequent studies and experimental verification.